Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia.

Falls are a leading cause of morbidity and mortality in older adults with dementia residing in long-term care. Having access to a frequently updated and accurate estimate of the likelihood of a fall over a short time frame for each resident will enable care staff to provide targeted interventions to prevent falls and resulting injuries. To this end, machine learning models to estimate and frequently update the risk of a fall within the next 4 weeks were trained on longitudinal data from 54 older adult participants with dementia. Data from each participant included baseline clinical assessments of gait, mobility, and fall risk at the time of admission, daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system. Systematic ablations investigated the effects of various hyperparameters and feature sets and experimentally identified differential contributions from baseline clinical assessments, ambient gait analysis, and daily medication intake. In leave-one-subject-out cross-validation, the best performing model predicts the likelihood of a fall over the next 4 weeks with a sensitivity and specificity of 72.8 and 73.2, respectively, and achieved an area under the receiver operating characteristic curve (AUROC) of 76.2. By contrast, the best model excluding ambient gait features achieved an AUROC of 56.2 with a sensitivity and specificity of 51.9 and 54.0, respectively. Future research will focus on externally validating these findings to prepare for the implementation of this technology to reduce fall and fall-related injuries in long-term care.

Exploring the perspectives of outpatient rehabilitation clinicians on the challenges with monitoring patient health, function and activity in the community.

PURPOSE: Rehabilitation clinicians need information about patient activities in the home/community to inform care. Despite active efforts to develop technologies that can meet this need, clinicians' perspectives regarding how information is collected and used in outpatient rehabilitation have not been comprehensively described. Therefore, we aimed to describe: (1) what data pertaining to a patient's health, function and activity in their home/community are currently collected in outpatient rehabilitation, (2) how these data can impact clinical decisions, and (3) what challenges clinicians encounter when they manage the care of outpatients based on this information. MATERIALS AND METHODS: Eight clinicians working in outpatient rehabilitation programs completed qualitative interviews that were analyzed using an inductive thematic analysis. RESULTS: Four themes were identified: "Nature of data about a patient's health, function and activity in the home/community and how it is collected by clinicians," "Value of data from the home/community," "Perceived drawbacks of current data collection methods," and "Improving data collection to understand patient trajectory." CONCLUSIONS: Clinicians described the importance of understanding patient activities in the home/community, but perspectives varied regarding the suitability of current methods. These perceptions may inform the design of solutions to bridge the gap between the clinic and the community in outpatient rehabilitation.Implications for rehabilitationClinical decision-making in outpatient rehabilitation is guided by verbal and written reports about a patient's health and function in the community and adherence to treatment plans.Differing perceptions on the suitability of current data collection methods indicate that the development of new solutions, such as rehabilitation technologies, needs to carefully consider clinician workflows and what data are perceived as meaningful.Potentially impactful directions for new solutions include providing well validated data on adherence, movement quality, or longitudinal progression, presented in formats that match clinical decision criteria.

Ecological Momentary Assessment of Depression in People With Advanced Dementia: Longitudinal Pilot Study.

BACKGROUND: Barriers to assessing depression in advanced dementia include the presence of informant and patient recall biases. Ecological momentary assessment provides an improved approach for mood assessment by collecting observations in intervals throughout the day, decreasing recall bias, and increasing ecological validity. OBJECTIVE: This study aims to evaluate the feasibility, reliability, and validity of the modified 4-item Cornell Scale for Depression in Dementia for Momentary Assessment (mCSDD4-MA) tool to assess depression in patients with advanced dementia. METHODS: A intensive longitudinal pilot study design was used. A total of 12 participants with advanced dementia were enrolled from an inpatient psychogeriatric unit. Participants were assessed using clinical depression assessments at admission and discharge. Research staff recorded observations four times a day for 6 weeks on phones with access to the mCSDD4-MA tool. Descriptive data related to feasibility were reported (ie, completion rates). Statistical models were used to examine the interrater reliability and construct and predictive validity of the data. RESULTS: Overall, 1923 observations were completed, representing 55.06% (1923/3496) of all rating opportunities with 2 raters and 66.01% (1923/2913) with at least one rater. Moderate interrater reliability was demonstrated for all items, except for lack of interest. Moderate correlations were observed between observers and patient-reported outcomes, where observers reported fewer symptoms relative to participants' self-reports. Several items were associated with and able to predict depression. CONCLUSIONS: The mCSDD4-MA tool was feasible to use, and most items in the tool showed moderate reliability and validity for assessing depression in dementia. Repeated and real-time depression assessment in advanced dementia holds promise for the identification of clinical depression and depressive symptoms.

Correlating natural language processing and automated speech analysis with clinician assessment to quantify speech-language changes in mild cognitive impairment and Alzheimer's dementia.

BACKGROUND: Language impairment is an important marker of neurodegenerative disorders. Despite this, there is no universal system of terminology used to describe these impairments and large inter-rater variability can exist between clinicians assessing language. The use of natural language processing (NLP) and automated speech analysis (ASA) is emerging as a novel and potentially more objective method to assess language in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). No studies have analyzed how variables extracted through NLP and ASA might also be correlated to language impairments identified by a clinician. METHODS: Audio recordings (n=30) from participants with AD, MCI, and controls were rated by clinicians for word-finding difficulty, incoherence, perseveration, and errors in speech. Speech recordings were also transcribed, and linguistic and acoustic variables were extracted through NLP and ASA. Correlations between clinician-rated speech characteristics and the variables were compared using Spearman's correlation. Exploratory factor analysis was applied to find common factors between variables for each speech characteristic. RESULTS: Clinician agreement was high in three of the four speech characteristics: word-finding difficulty (ICC = 0.92, p<0.001), incoherence (ICC = 0.91, p<0.001), and perseveration (ICC = 0.88, p<0.001). Word-finding difficulty and incoherence were useful constructs at distinguishing MCI and AD from controls, while perseveration and speech errors were less relevant. Word-finding difficulty as a construct was explained by three factors, including number and duration of pauses, word duration, and syntactic complexity. Incoherence was explained by two factors, including increased average word duration, use of past tense, and changes in age of acquisition, and more negative valence. CONCLUSIONS: Variables extracted through automated acoustic and linguistic analysis of MCI and AD speech were significantly correlated with clinician ratings of speech and language characteristics. Our results suggest that correlating NLP and ASA with clinician observations is an objective and novel approach to measuring speech and language changes in neurodegenerative disorders.

Behaviour assessment tools in long-term care homes in Canada: a survey.

OBJECTIVE: Many people living in long-term care homes (LTCH) experience changes in behaviour termed the behavioural and psychological symptoms of dementia (BPSD). The valid and reliable assessment of BPSD is essential to guide treatment and monitor the effect of interventions. The aim of this study was to identify behavioural assessment tools implemented in LTCH and factors that impact on their use in clinical care. METHODS: We completed an online mixed-design survey of 300 randomly selected Canadian LTCH between September and November 2018. Respondents were asked to report tools used, reasons for use, methods of administration, training/supports available, confidence in use and challenges faced. Survey results were summarized descriptively and the correlation between implementation supports and confidence examined. Free-text responses were analysed qualitatively. RESULTS: Of 300 LTCH invited to participate, 103 completed the survey. Homes reported using a mean 2.2 ± 1.1 (range 0-7) different tools. The two most commonly used tools were the Dementia Observation System (DOS) and Cohen-Mansfield Agitation Inventory (CMAI). Overall confidence in most aspects of tool use was reported to be high, with workload identified as the greatest challenge. Training and supports correlated with confidence in tool use. Qualitative findings indicate tools provide valuable data to understand behaviours, facilitate team communication, target interventions and track outcomes. CONCLUSIONS: Behavioural assessment tools, in particular a direct observation tool, are widely used in clinical care in Canadian LTCH. Education, enhanced resources, leadership support and applications of technology represent opportunities to improve their use.

Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data.

BACKGROUND: Parkinsonism is common in people with dementia, and is associated with neurodegenerative and vascular changes in the brain, or with exposure to antipsychotic or other dopamine antagonist medications. The detection of parkinsonian changes to gait may provide an opportunity to intervene and address reversible causes. In this study, we investigate the use of a vision-based system as an unobtrusive means to assess severity of parkinsonism in gait. METHODS: Videos of walking bouts of natural gait were collected in a specialized dementia unit using a Microsoft Kinect sensor and onboard color camera, and were processed to extract sixteen 3D and eight 2D gait features. Univariate regression to gait quality, as rated on the Unified Parkinson's Disease Rating Scale (UPDRS) and Simpson-Angus Scale (SAS), was used to identify gait features significantly correlated to these clinical scores for inclusion in multivariate models. Multivariate ordinal logistic regression was subsequently performed and the relative contribution of each gait feature for regression to UPDRS-gait and SAS-gait scores was assessed. RESULTS: Four hundred one walking bouts from 14 older adults with dementia were included in the analysis. Multivariate ordinal logistic regression models incorporating selected 2D or 3D gait features attained similar accuracies: the UPDRS-gait regression models achieved accuracies of 61.4 and 62.1% for 2D and 3D features, respectively. Similarly, the SAS-gait models achieved accuracies of 47.4 and 48.5% with 2D or 3D gait features, respectively. CONCLUSIONS: Gait features extracted from both 2D and 3D videos are correlated to UPDRS-gait and SAS-gait scores of parkinsonism severity in gait. Vision-based systems have the potential to be used as tools for longitudinal monitoring of parkinsonism in residential settings.

Vision-Based Assessment of Gait Features Associated With Falls in People With Dementia.

BACKGROUND: Gait impairments contribute to falls in people with dementia. In this study, we used a vision-based system to record episodes of walking over a 2-week period as participants moved naturally around their environment, and from these calculated spatiotemporal, stability, symmetry, and acceleration gait features. The aim of this study was to determine whether features of gait extracted from a vision-based system are associated with falls, and which of these features are most strongly associated with falling. METHODS: Fifty-two people with dementia admitted to a specialized dementia unit participated in this study. Thirty different features describing baseline gait were extracted from Kinect recordings of natural gait over a 2-week period. Baseline clinical and demographic measures were collected, and falls were tracked throughout the participants' admission. RESULTS: A total of 1,744 gait episodes were recorded (mean 33.5 ± 23.0 per participant) over a 2-week baseline period. There were a total of 78 falls during the study period (range 0-10). In single variable analyses, the estimated lateral margin of stability, step width, and step time variability were significantly associated with the number of falls during admission. In a multivariate model controlling for clinical and demographic variables, the estimated lateral margin of stability (p = .01) was remained associated with number of falls. CONCLUSIONS: Information about gait can be extracted from vision-based recordings of natural walking. In particular, the lateral margin of stability, a measure of lateral gait stability, is an important marker of short-term falls risk.

Key factors for the assessment of mobility in advanced dementia: A consensus approach.

INTRODUCTION: Loss of mobility is common in advanced dementia and has important negative consequences related to fall risk, loss of independence, and lack of participation in meaningful activities. The causes of decline are multifactorial, including disease-specific changes in motor function, behavior, and cognition. To optimize clinical management of mobility, there is a need to better characterize capacity for safe and independent mobility. This study aimed to identify key factors that impact on mobility in dementia. METHODS: Expert input was gathered using a modified Delphi consensus approach. The primary criterion for participation was specialist knowledge in mobility or dementia, either as a clinician or a researcher. Participants rated elements of mobility for importance and feasibility of assessment in advanced dementia and prioritized items for inclusion in a mobility staging tool. Descriptive statistics and qualitative content analysis were used to summarize responses. RESULTS: Thirty-six experts completed the first survey with an 80% retention rate over three rounds. One-third of 61 items reached consensus for being both important and feasible to assess, representing five categories of elements. Items reaching agreement for a staging tool included walking, parkinsonism, gait, impulsivity, fall history, agitation, transfers, and posture control. DISCUSSION: This study highlights the need for a multidimensional, dementia-specific approach to mobility assessment. Results have implications for development of assessment methods and management guidelines to support the clinical care of mobility impairment in people with dementia.

Challenges in Collecting Big Data in A Clinical Environment with Vulnerable Population: Lessons Learned from A Study Using A Multi-modal Sensors Platform.

Agitation is one of the most common behavioural and psychological symptoms in people living with dementia (PLwD). This behaviour can cause tremendous stress and anxiety on family caregivers and healthcare providers. Direct observation of PLwD is the traditional way to measure episodes of agitation. However, this method is subjective, bias-prone and timeconsuming. Importantly, it does not predict the onset of the agitation. Therefore, there is a need to develop a continuous monitoring system that can detect and/or predict the onset of agitation. In this study, a multi-modal sensor platform with video cameras, motion and door sensors, wristbands and pressure mats were set up in a hospital-based dementia behavioural care unit to develop a predictive system to identify the onset of agitation. The research team faced several barriers in the development and initiation of the study, namely addressing concerns about the study ethics, logistics and costs of study activities, device design for PLwD and limitations of its use in the hospital. In this paper, the strategies and methodologies that were implemented to address these challenges are discussed for consideration by future researchers who will conduct similar studies in a hospital setting.

Quantitative Mobility Assessment for Fall Risk Prediction in Dementia: A Systematic Review.

BACKGROUND: Impairments of gait and balance often progress through the course of dementia, and are associated with increased risk of falls. SUMMARY: This systematic review provides a critical analysis of the evidence linking quantitative measures of gait and balance to fall risk in older adults with dementia. Various instrumented measures of gait and postural stability including gait speed and non-instrumented performance measures including Timed Up and Go were shown to be capable of distinguishing fallers from non-fallers. Key Messages: Existing reviews indicate that impairments of gait and balance are associated with increased risk of falls in cognitively intact older people. There are inconsistencies, however, regarding the characteristics most predictive of a fall. In order to advance fall prevention efforts, there is an important need to understand the relationship between gait, balance, and fall risk, particularly in high-risk populations such as individuals with dementia.

Initiation of Antidepressant Medication After Hip Fracture in Community-Dwelling Older Adults.

OBJECTIVE: To evaluate the incidence, characteristics, and correlates of antidepressant drug therapy initiation among community-dwelling older adults following hip fracture. DESIGN: Retrospective cohort study using linked, population-based administrative data. SETTING: Province of Ontario, Canada. PARTICIPANTS: Older adults, aged 65 years or older, with a hip fracture and hip fracture surgery between April 1, 2003, and February 28, 2011. The study sample was restricted to individuals who returned home following surgery and who had not been dispensed an antidepressant in the year prior to their fracture (N=25,436). MEASUREMENTS: We determined the incidence of new antidepressant use defined by the dispensing of antidepressant drug therapy within 90 days of discharge home. We identified independent correlates of antidepressant initiation using multivariate regression. RESULTS: Overall, antidepressants were newly initiated in 8.8% of older adults with hip fracture in the 90 days following hospital discharge. There was a statistically significant, 1.3-fold increase in incidence of antidepressant prescribing from 2003 to 2010. Trazodone, frequently prescribed at a low dose, accounted for 39.0% of newly dispensed antidepressants, followed by selective serotonin reuptake inhibitors (36.9%). Rehabilitation admission, psychiatric evaluation, a diagnosis of dementia, and baseline benzodiazepine use were the strongest independent correlates of antidepressant initiation. CONCLUSION: The period after a hip fracture is associated with a high rate of initiation of antidepressant therapy. The data raise the possibility that antidepressants are frequently prescribed off-label in these patients. Further research is needed to investigate the safety and efficacy of antidepressant use in this vulnerable population.